Пример #1
0
def combine_sailfish(samples):
    work_dir = dd.get_in_samples(samples, dd.get_work_dir)
    sailfish_dir = os.path.join(work_dir, "sailfish")
    gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
    dont_combine, to_combine = partition(dd.get_sailfish,
                                         dd.sample_data_iterator(samples),
                                         True)
    if not to_combine:
        return samples

    tidy_file = os.path.join(sailfish_dir, "combined.sf")
    transcript_tpm_file = os.path.join(sailfish_dir, "combined.isoform.sf.tpm")
    gene_tpm_file = os.path.join(sailfish_dir, "combined.gene.sf.tpm")
    tx2gene = os.path.join(sailfish_dir, "tx2gene.csv")
    if not all([
            file_exists(x)
            for x in [gene_tpm_file, tidy_file, transcript_tpm_file, tx2gene]
    ]):
        logger.info("Combining count files into %s." % tidy_file)
        df = pd.DataFrame()
        for data in to_combine:
            sailfish_file = dd.get_sailfish(data)
            samplename = dd.get_sample_name(data)
            new_df = _sailfish_expression_parser(sailfish_file, samplename)
            if df.empty:
                df = new_df
            else:
                df = rbind([df, new_df])
        df["id"] = df.index
        # some versions of the transcript annotations can have duplicated entries
        df = df.drop_duplicates(["id", "sample"])
        with file_transaction(tidy_file) as tx_out_file:
            df.to_csv(tx_out_file, sep="\t", index_label="name")
        with file_transaction(transcript_tpm_file) as tx_out_file:
            df.pivot("id", "sample", "tpm").to_csv(tx_out_file, sep="\t")
        with file_transaction(gene_tpm_file) as tx_out_file:
            pivot = df.pivot("id", "sample", "tpm")
            tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
                                         orient="index")
            tdf.columns = ["gene_id"]
            pivot = pivot.join(tdf)
            pivot = pivot.groupby("gene_id").agg(np.sum)
            pivot.to_csv(tx_out_file, sep="\t")
        tx2gene = gtf.tx2genefile(gtf_file, tx2gene)
        logger.info("Finished combining count files into %s." % tidy_file)

    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_sailfish_tidy(data, tidy_file)
        data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
        data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
        data = dd.set_tx2gene(data, tx2gene)
        updated_samples.append([data])
    return updated_samples
Пример #2
0
def combine_sailfish(samples):
    work_dir = dd.get_in_samples(samples, dd.get_work_dir)
    sailfish_dir = os.path.join(work_dir, "sailfish")
    gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
    dont_combine, to_combine = partition(dd.get_sailfish,
                                         dd.sample_data_iterator(samples), True)
    if not to_combine:
        return samples

    tidy_file = os.path.join(sailfish_dir, "combined.sf")
    transcript_tpm_file = os.path.join(sailfish_dir, "combined.isoform.sf.tpm")
    gene_tpm_file = os.path.join(sailfish_dir, "combined.gene.sf.tpm")
    tx2gene = os.path.join(sailfish_dir, "tx2gene.csv")
    if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
                                         transcript_tpm_file, tx2gene]]):
        logger.info("Combining count files into %s." % tidy_file)
        df = pd.DataFrame()
        for data in to_combine:
            sailfish_file = dd.get_sailfish(data)
            samplename = dd.get_sample_name(data)
            new_df = _sailfish_expression_parser(sailfish_file, samplename)
            if df.empty:
                df = new_df
            else:
                df = rbind([df, new_df])
        df["id"] = df.index
        # some versions of the transcript annotations can have duplicated entries
        df = df.drop_duplicates(["id", "sample"])
        with file_transaction(tidy_file) as tx_out_file:
            df.to_csv(tx_out_file, sep="\t", index_label="name")
        with file_transaction(transcript_tpm_file) as  tx_out_file:
            df.pivot("id", "sample", "tpm").to_csv(tx_out_file, sep="\t")
        with file_transaction(gene_tpm_file) as  tx_out_file:
            pivot = df.pivot("id", "sample", "tpm")
            tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
                                         orient="index")
            tdf.columns = ["gene_id"]
            pivot = pivot.join(tdf)
            pivot = pivot.groupby("gene_id").agg(np.sum)
            pivot.to_csv(tx_out_file, sep="\t")
        tx2gene = gtf.tx2genefile(gtf_file, tx2gene)
        logger.info("Finished combining count files into %s." % tidy_file)

    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_sailfish_tidy(data, tidy_file)
        data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
        data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
        data = dd.set_tx2gene(data, tx2gene)
        updated_samples.append([data])
    return updated_samples
Пример #3
0
def combine_sailfish(samples):
    work_dir = dd.get_in_samples(samples, dd.get_work_dir)
    gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
    dont_combine, to_combine = partition(dd.get_sailfish,
                                         dd.sample_data_iterator(samples), True)
    if not to_combine:
        return samples

    tidy_file = os.path.join(work_dir, "sailfish", "combined.sf")
    transcript_tpm_file = os.path.join(work_dir, "sailfish",
                                       "combined.isoform.sf.tpm")
    gene_tpm_file = os.path.join(work_dir, "sailfish",
                                 "combined.gene.sf.tpm")
    if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
                                         transcript_tpm_file]]):
        df = pd.DataFrame()
        for data in to_combine:
            sailfish_file = dd.get_sailfish(data)
            samplename = dd.get_sample_name(data)
            new_df = _sailfish_expression_parser(sailfish_file, samplename)
            if df.empty:
                df = new_df
            else:
                df = rbind([df, new_df])
        with file_transaction(tidy_file) as tx_out_file:
            df.to_csv(tx_out_file, sep="\t", index_label="name")
        with file_transaction(transcript_tpm_file) as  tx_out_file:
            df.pivot(None, "sample", "tpm").to_csv(tx_out_file, sep="\t")
        with file_transaction(gene_tpm_file) as  tx_out_file:
            pivot = df.pivot(None, "sample", "tpm")
            tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
                                         orient="index")
            tdf.columns = ["gene_id"]
            pivot = pivot.join(tdf)
            pivot = pivot.groupby("gene_id").agg(np.sum)
            pivot.to_csv(tx_out_file, sep="\t")

    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_sailfish_tidy(data, tidy_file)
        data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
        data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
        updated_samples.append([data])
    return updated_samples
Пример #4
0
def combine_sailfish(samples):
    work_dir = dd.get_in_samples(samples, dd.get_work_dir)
    gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
    dont_combine, to_combine = partition(dd.get_sailfish,
                                         dd.sample_data_iterator(samples), True)
    if not to_combine:
        return samples

    tidy_file = os.path.join(work_dir, "sailfish", "combined.sf")
    transcript_tpm_file = os.path.join(work_dir, "sailfish",
                                       "combined.isoform.sf.tpm")
    gene_tpm_file = os.path.join(work_dir, "sailfish",
                                 "combined.gene.sf.tpm")
    if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
                                         transcript_tpm_file]]):
        df = pd.DataFrame()
        for data in to_combine:
            sailfish_file = dd.get_sailfish(data)
            samplename = dd.get_sample_name(data)
            new_df = _sailfish_expression_parser(sailfish_file, samplename)
            if df.empty:
                df = new_df
            else:
                df = rbind([df, new_df])
        with file_transaction(tidy_file) as tx_out_file:
            df.to_csv(tx_out_file, sep="\t", index_label="name")
        with file_transaction(transcript_tpm_file) as  tx_out_file:
            df.pivot(None, "sample", "tpm").to_csv(tx_out_file, sep="\t")
        with file_transaction(gene_tpm_file) as  tx_out_file:
            pivot = df.pivot(None, "sample", "tpm")
            tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
                                         orient="index")
            tdf.columns = ["gene_id"]
            pivot = pivot.join(tdf)
            pivot = pivot.groupby("gene_id").agg(np.sum)
            pivot.to_csv(tx_out_file, sep="\t")

    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_sailfish_tidy(data, tidy_file)
        data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
        data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
        updated_samples.append([data])
    return updated_samples